from load_config import ConfigLoader
import h5py
import numpy as np
from tqdm import tqdm
import os
from utils import del_fileordir

def cal_middleval(p_text, key):
    """ 计算sbox(p[3] xor k[3])
    注意要传两个数字型
    """
    s_box = [[0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76],
             [0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0],
             [0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15],
             [0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75],
             [0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84],
             [0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF],
             [0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8],
             [0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2],
             [0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73],
             [0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB],
             [0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79],
             [0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08],
             [0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A],
             [0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E],
             [0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF],
             [0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16]]

    m_val = p_text ^ key
    row = m_val >> 4
    col = m_val & 0x0F
    return int(s_box[row][col])

def hamming_weight(x):
    """ 计算汉明重量
    """
    return bin(x).count('1')

class DataGroup:
    def __init__(self, configs):

        # 读取配置
        self.configs = configs
        self.dataset_cfg = self.configs["datasets"]
        trainset_cfg = self.dataset_cfg["trainset"]
        self.output_path = self.dataset_cfg["output_path"]
        if (self.output_path == None):
            self.output_path = "datasets/group"
        # 读取数据集
        f = h5py.File(trainset_cfg["path"], 'r')

        self.traces = np.array(f['Profiling_traces/traces'])

        metadatas = np.array(f['Profiling_traces/metadata'])
        plaintext_list = []
        for metadata in metadatas:
            plaintext_list.append(metadata[0][2])
        self.plain_text = np.array(plaintext_list)

    def generate_txt_group(self):
        if (os.path.exists(self.output_path)):
            del_fileordir(self.output_path)
        # for 256 keys to calculate the middle value
        for k in tqdm(range(256)):
            for i in range(len(self.plain_text)):
                middle_val = cal_middleval(int(self.plain_text[i]), k)
                # calculate the msb of middle value
                if self.dataset_cfg["label_type"] == 2:
                    msb = (middle_val >> 7) & 0x01
                    h = msb
                elif self.dataset_cfg["label_type"] == 9:
                    h = hamming_weight(middle_val)
                # if directory not exist, create it
                if not os.path.exists(self.output_path + '/' + str(k)):
                    os.makedirs(self.output_path + '/' + str(k))
                # write traces in k/msb.txt
                with open(self.output_path + '/' + str(k) + '/' + str(h) + '.txt', 'a') as f:
                    for t in self.traces[i]:
                        f.write(str(t) + ' ')
                    f.write('\n')

    def check_group(self):
        if (os.path.exists(self.output_path)):
            if (len(os.listdir(self.output_path)) == 256):
                if (len(os.listdir(self.output_path + "/0/")) == int(self.dataset_cfg["label_type"])):
                    return True
        return False

# def main():
#     config_loader = ConfigLoader()
#     configs = config_loader.get_configs()
# 
#     # read h5 file
#     dataset_cfg = configs["datasets"]
#     trainset_cfg = dataset_cfg["trainset"]
#     f = h5py.File(trainset_cfg["path"], 'r')
#     traces = np.array(f['Profiling_traces/traces'])
#     metadatas = np.array(f['Profiling_traces/metadata'])
#     plaintext_list = []
#     for metadata in metadatas:
#         plaintext_list.append(metadata[0][2])
#     plain_text = np.array(plaintext_list)
# 
#     # for 256 keys to calculate the middle value
#     for k in tqdm(range(256)):
#         for i in range(len(plain_text)):
#             middle_val = cal_middleval(int(plain_text[i]), k)
#             # calculate the msb of middle value
#             if dataset_cfg["label_type"] == 2:
#                 msb = (middle_val >> 7) & 0x01
#                 h = msb
#             elif dataset_cfg["label_type"] == 9:
#                 h = hamming_weight(middle_val)
#             # if directory not exist, create it
#             if not os.path.exists("../../../datasets/group" + '/' + str(k)):
#                 os.makedirs("../../../datasets/group" + '/' + str(k))
#             # write traces in k/msb.txt
#             with open("../../../datasets/group" + '/' + str(k) + '/' + str(h) + '.txt', 'a') as f:
#                 for t in traces[i]:
#                     f.write(str(t) + ' ')
#                 f.write('\n')
# 
# if __name__ == '__main__':
#     main()
